# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
from . import recompute_grads


def compute_gradients(total_loss, recompute=False, var_partition=False):
    grads = recompute_grads.gradients( total_loss, tf.trainable_variables(), checkpoints='self_define' ) 
    grads_convert = []
    for g in grads:
        if g is not None:
            if isinstance( g, tf.IndexedSlices ):
                g = tf.convert_to_tensor(g)
            grads_convert.append(g)   

    grads = grads_convert
    grads_and_vars = list(zip( grads, tf.trainable_variables() ))

    return grads_and_vars




